
import mysql.connector
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sb
import numpy as np
from enum import Enum


cnx = mysql.connector.connect(user='user', password='root',
                              host='127.0.0.1',
                              database='data_analytics')
cursor = cnx.cursor(dictionary = True)


sql_query = '''
    SELECT DISTINCT
        StockPrices.date, 
        StockPrices.stock_id, 
        StockPrices.open, 
        StockPrices.close, 
        StockPrices.high, 
        StockPrices.low, 
        StockPrices.volume, 
        StocksIndustries.industry_id 
    FROM 
        StocksIndustries 
    INNER JOIN StockPrices USING(stock_id)
'''
cursor.execute(sql_query)
result = cursor.fetchall()
df = pd.DataFrame(result)
df = df[::-1].reset_index(drop=True)


df['return'] = df.groupby(['stock_id', 'industry_id'])['close'].pct_change()
df['cum_returns'] = df.groupby(['stock_id', 'industry_id'])['return'].transform(lambda x: (1 + x).cumprod())
df


groups = df.groupby('industry_id')
industry_ids = df['industry_id'].dropna().unique()
fig, axes = plt.subplots(nrows=len(industry_ids), ncols=1, figsize=(10, 5*len(industry_ids)), sharex=True)
# Iterate over each industry_id and plot close values
for i, industry_id in enumerate(industry_ids):
    ax = axes[i]
    industry_data = groups.get_group(industry_id)
    # Iterate over each stock in the industry and plot the close values
    for stock_id, stock_data in industry_data.groupby('stock_id'):
        ax.plot(stock_data['date'], stock_data['close'], label=stock_id)

    ax.set_title(industry_id)
    ax.set_xlabel('Date')
    ax.set_ylabel('Close Price')
    ax.legend()


# Plot Correlation Coefficient of Closing Price
fig, axes = plt.subplots(nrows=len(industry_ids), ncols=1, figsize=(10, 5*len(industry_ids)), sharex=True)

for i, industry_id in enumerate(industry_ids):
    ax = axes[i]
    ax.set_title(industry_id)
    industry_data = groups.get_group(industry_id)
    # Pivot the DataFrame to have stock_id as columns and closing prices as values
    close_df = industry_data.pivot(index='date', columns='stock_id', values='close')
    pearsoncorr = close_df.corr(method ='pearson')
    sb.heatmap(pearsoncorr, ax = ax, xticklabels=pearsoncorr.columns, yticklabels=pearsoncorr.columns, cmap='RdBu_r', annot=True, linewidth=0.5)


fig, axes = plt.subplots(nrows=len(industry_ids), ncols=1, figsize=(10, 5*len(industry_ids)), sharex=True)
# Iterate over each industry_id and plot cumulative returns values
for i, industry_id in enumerate(industry_ids):
    ax = axes[i]
    industry_data = groups.get_group(industry_id)
    # Iterate over each stock in the industry and plot the cumulative returns values
    for stock_id, stock_data in industry_data.groupby('stock_id'):
        ax.plot(stock_data['date'], stock_data['cum_returns'], label=stock_id)

    ax.set_title(industry_id)
    ax.set_xlabel('Date')
    ax.set_ylabel('Cumulative Returns Price')
    ax.legend()


fig, axes = plt.subplots(nrows=len(industry_ids), ncols=1, figsize=(10, 5*len(industry_ids)), sharex=True)

for i, industry_id in enumerate(industry_ids):
    ax = axes[i]
    ax.set_title(industry_id)
    industry_data = groups.get_group(industry_id)
    close_df = industry_data.pivot(index='date', columns='stock_id', values='cum_returns')
    pearsoncorr = close_df.corr(method ='pearson')
    sb.heatmap(pearsoncorr, ax = ax, xticklabels=pearsoncorr.columns, yticklabels=pearsoncorr.columns, cmap='RdBu_r', annot=True, linewidth=0.5)


sql_query = '''
    SELECT DISTINCT
        *
    FROM 
        StockFinancialData 
    WHERE stock_id = 'AAPL' OR stock_id = 'NVDA';
'''
cursor.execute(sql_query)
result = cursor.fetchall()
df = pd.DataFrame(result)


df.loc[df['stock_id'] == 'AAPL', 'year'] -= 1
df = df[df['year'] != -1]
df = df[df['year'] != 9]
df


convert_index_to_years = {
    0: 2022,
    1: 2021,
    2: 2020,
    3: 2019,
    4: 2018,
    5: 2017,
    6: 2016,
    7: 2015,
    8: 2014
}

df['year'] = df['year'].replace(convert_index_to_years)
df


class Facts(Enum):
    TOTAL_NET_SALES = 'RevenueFromContractWithCustomerExcludingAssessedTax'
    GROSS_PROFIT = 'GrossProfit'
    OPERATING_PROFIT_LOSS = 'OperatingIncomeLoss'
    EARNINGS_PER_SHARE_DILUTED = 'EarningsPerShareDiluted' 
    CURRENT_ASSETS = 'AssetsCurrent'
    NON_CURRENT_ASSETS = 'AssetsNoncurrent'
    CURRENT_LIABILITIES = 'LiabilitiesCurrent'
    NON_CURRENT_LIABILITES = 'LiabilitiesNoncurrent'
    DIVIDENDS = 'CommonStockDividendsPerShareDeclared' 
    CASH_GENERATED_BY_OPERATING_ACTIVITIES = 'NetCashProvidedByUsedInOperatingActivities'
    COST_OF_SALES = 'CostOfGoodsAndServicesSold' 
    CURRENT_ASSETS_INVENTORIES = 'InventoryNet'
    CURRENT_ASSETS_DEBTORS = 'AccountsReceivableNetCurrent'
    CURRENT_ASSETS_CASH = 'CashAndCashEquivalentsAtCarryingValue'
    CURRENT_ASSETS_OTHERS  = 'OtherAssetsCurrent'
    CURRENT_LIABILTIES_LOANS = 'LongTermDebtCurrent'
    CURRENT_LIABILITIES_CREDITORS = 'AccountsPayableCurrent' 
    CURRENT_LIABILITIES_OTHERS = 'OtherLiabilitiesCurrent'
    NON_CURRENT_LOANS = 'LongTermDebtNoncurrent'


data = pd.pivot_table(df, values = 'value', index = ['fact', 'stock_id'], columns = ['year'])
data


def aggregate_values(fact, stock_id):
    return data.loc[fact, stock_id] / 1000000

rename_indexes = {
    Facts.TOTAL_NET_SALES.value : 'Total Net Sales',
    Facts.GROSS_PROFIT.value : 'Gross Profit',
    Facts.OPERATING_PROFIT_LOSS.value : 'Operating Profit',
    Facts.EARNINGS_PER_SHARE_DILUTED.value : 'Earnings Per Share',
    Facts.CURRENT_ASSETS.value : 'Current Assets',
    Facts.NON_CURRENT_ASSETS.value : 'Non-current Assets',
    Facts.CURRENT_LIABILITIES.value : 'Current Liabilities',
    Facts.NON_CURRENT_LIABILITES.value : 'Non-current Liabilities',
    Facts.DIVIDENDS.value : 'Dividends' ,
    Facts.CASH_GENERATED_BY_OPERATING_ACTIVITIES.value : 'Net Cash Flow from Operating Activities',
    Facts.COST_OF_SALES.value : 'Cost of Sales',
    Facts.CURRENT_ASSETS_INVENTORIES.value : 'Inventories',
    Facts.CURRENT_ASSETS_DEBTORS.value : 'Trade Debtors',
    Facts.CURRENT_ASSETS_CASH.value : 'Cash and Cash Equivalents',
    Facts.CURRENT_ASSETS_OTHERS.value  : 'Other Current Assets',
    Facts.CURRENT_LIABILTIES_LOANS.value : 'Current Long Term Debt',
    Facts.CURRENT_LIABILITIES_CREDITORS.value : 'Trade Creditors',
    Facts.CURRENT_LIABILITIES_OTHERS.value : 'Other Current Liabilities',
    Facts.NON_CURRENT_LOANS.value : 'Non-current Long Term Debt'
}

stock_ids = ['AAPL', 'NVDA']
data = data.rename(index = rename_indexes)
for stock_id in stock_ids:
    for fact in list(Facts):
        if fact.value == Facts.DIVIDENDS.value or fact.value == Facts.EARNINGS_PER_SHARE_DILUTED.value:
            continue
        data.loc[rename_indexes[fact.value], stock_id]= data.loc[rename_indexes[fact.value], stock_id] / 1000000
        
data


def create_graph(metrics, table, ylabel = 'Percentage %'):
    fig, axes = plt.subplots(nrows=len(metrics), ncols=1, figsize=(10, 5*len(metrics)), sharex=True)
    for i, metric in enumerate(metrics):
        ax = axes[i] if len(metrics) > 1 else axes
        for stock_id in stock_ids:
            ax.plot(table.columns, table.loc[metric, stock_id], label = stock_id)

            ax.set_title(metric)
            ax.set_xlabel('Years')
            ax.set_ylabel(ylabel)
            ax.legend()


# Missing - Profit attributable to equity shareholders, closing share price, earnings per share, dividend per share, operating cash flow per share
fact = ['Sales Revenue', 'Operating Profit', 'Net Assets', 'Return on Net Assets %', 'Current Liabilities Ratio %']
stock_ids = ['AAPL', 'NVDA']
years = data.columns

multi_index = pd.MultiIndex.from_product([fact, stock_ids], names=['fact', 'stock_id'])
summary_table = pd.DataFrame(index=multi_index, columns=years)
for x in years:
    for stock_id in stock_ids:
        summary_table.loc[('Sales Revenue', stock_id), x] = data.loc[('Total Net Sales', stock_id), x]
        summary_table.loc[('Operating Profit', stock_id), x] = data.loc[('Operating Profit', stock_id), x]
        summary_table.loc[('Net Assets', stock_id), x] = data.loc[('Non-current Assets', stock_id), x] + data.loc[('Current Assets', stock_id), x] - data.loc[('Current Liabilities', stock_id), x]
        summary_table.loc[('Return on Net Assets %', stock_id), x] = (summary_table.loc[('Operating Profit', stock_id), x] / summary_table.loc[('Net Assets', stock_id), x]) * 100
        summary_table.loc[('Current Liabilities Ratio %', stock_id), x] = (data.loc[('Current Liabilities', stock_id), x] / summary_table.loc[('Net Assets', stock_id), x]) * 100

summary_table


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Return on Net Assets %', 'Current Liabilities Ratio %']

ax1.plot(summary_table.columns, summary_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(summary_table.columns, summary_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.set_ylabel(metrics[0], color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(summary_table.columns, summary_table.loc[metrics[1], 'AAPL'], label=f'AAPL - {metrics[1]}', linestyle='--', marker='x', color='#5DBB63')
ax2.plot(summary_table.columns, summary_table.loc[metrics[1], 'NVDA'], label=f'NVDA - {metrics[1]}', linestyle='--', marker='x', color='#F28500')

ax2.set_ylabel(metrics[1], color='red')
ax2.tick_params(axis='y', labelcolor='red')
ax2.legend(loc='upper right')

plt.title('Return on Net Assets % and Current Liabilities Ratio % over Years')
plt.grid(True)
plt.show()


# Missing - Profit attributable to equity shareholders
fact = ['Sales Revenue', 'Gross Profit', 'Operating Profit', 'Gross Profit %', 'Operating Profit %', 'Sales Revenue % change', 'Gross Profit % change', 'Operating Profit % change', 'Gross vs Operating Ratio']
multi_index = pd.MultiIndex.from_product([fact, stock_ids], names=['fact', 'stock_id'])
income_statement_table = pd.DataFrame(index=multi_index, columns=years)

for x in years:
    for stock_id in stock_ids:
        income_statement_table.loc[('Sales Revenue', stock_id), x] = data.loc[('Total Net Sales', stock_id), x]
        income_statement_table.loc[('Gross Profit', stock_id), x] = data.loc[('Gross Profit', stock_id), x]
        income_statement_table.loc[('Operating Profit', stock_id), x] = data.loc[('Operating Profit', stock_id), x]
        income_statement_table.loc[('Gross Profit %', stock_id), x] = (income_statement_table.loc[('Gross Profit', stock_id), x] / income_statement_table.loc[('Sales Revenue', stock_id), x]) * 100
        income_statement_table.loc[('Operating Profit %', stock_id), x] = (income_statement_table.loc[('Operating Profit', stock_id), x] / income_statement_table.loc[('Sales Revenue', stock_id), x]) * 100
        income_statement_table.loc[('Gross vs Operating Ratio', stock_id), x] = income_statement_table.loc[('Gross Profit', stock_id), x] / income_statement_table.loc[('Operating Profit', stock_id), x]
        
for stock_id in stock_ids:
    income_statement_table.loc[('Sales Revenue % change', stock_id)] = income_statement_table.loc[('Sales Revenue', stock_id)].pct_change(periods = 1)
    income_statement_table.loc[('Gross Profit % change', stock_id)] = income_statement_table.loc[('Gross Profit', stock_id)].pct_change(periods = 1)
    income_statement_table.loc[('Operating Profit % change', stock_id)] = income_statement_table.loc[('Operating Profit', stock_id)].pct_change(periods = 1)

income_statement_table


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Gross Profit %', 'Operating Profit %']

ax1.plot(income_statement_table.columns, income_statement_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(income_statement_table.columns, income_statement_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')
ax1.plot(income_statement_table.columns, income_statement_table.loc[metrics[1], 'AAPL'], label=f'AAPL - {metrics[1]}', linestyle='--', marker='x', color='#5DBB63')
ax1.plot(income_statement_table.columns, income_statement_table.loc[metrics[1], 'NVDA'], label=f'NVDA - {metrics[1]}', linestyle='--', marker='x', color='#F28500')

ax1.set_xlabel('Years')
ax1.set_ylabel(metrics[0], color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')


plt.title('Gross Profit % and Operating Profit %')
plt.grid(True)
plt.show()


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Gross vs Operating Ratio']

ax1.plot(income_statement_table.columns, income_statement_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(income_statement_table.columns, income_statement_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

plt.title('Gross vs Operating Ratio')
plt.grid(True)
plt.show()


# Net Working Capital = Current Assets - Current Liabilities
# Net Assets = Non-current Assets + Current Assets - Current Liabilities
fact = ['Net Assets', 'Non-current Assets', 'Current Assets', 'Current Liabilities excluding loans', 'Net Working Capital', 'Debt', 'Non-current Assets %', 'Current Assets %', 'Current Liabilities %', 'Net Working Capital %', 'Debt %', 'Net Working Capital vs Debt Ratio']
multi_index = pd.MultiIndex.from_product([fact, stock_ids], names=['fact', 'stock_id'])
balance_sheet_table = pd.DataFrame(index=multi_index, columns=years)

for x in years:
    for stock_id in stock_ids:
        balance_sheet_table.loc[('Net Assets', stock_id), x] = summary_table.loc[('Net Assets', stock_id), x]
        balance_sheet_table.loc[('Non-current Assets', stock_id), x] = data.loc[('Non-current Assets', stock_id), x]
        balance_sheet_table.loc[('Current Assets', stock_id), x] = data.loc[('Current Assets', stock_id), x]
        balance_sheet_table.loc[('Current Liabilities excluding loans', stock_id), x] = data.loc[('Trade Creditors', stock_id), x] + data.loc[('Other Current Liabilities', stock_id), x]
        balance_sheet_table.loc[('Net Working Capital', stock_id), x] = data.loc[('Current Assets', stock_id), x] - (data.loc[('Trade Creditors', stock_id), x] + data.loc[('Other Current Liabilities', stock_id), x])
        balance_sheet_table.loc[('Debt', stock_id), x] = data.loc[('Current Long Term Debt', stock_id), x] + data.loc[('Non-current Long Term Debt', stock_id), x]
        balance_sheet_table.loc[('Non-current Assets %', stock_id), x] = (data.loc[('Non-current Assets', stock_id), x] / balance_sheet_table.loc[('Net Assets', stock_id), x]) * 100
        balance_sheet_table.loc[('Current Assets %', stock_id), x] = (data.loc[('Current Assets', stock_id), x] / balance_sheet_table.loc[('Net Assets', stock_id), x]) * 100
        balance_sheet_table.loc[('Current Liabilities %', stock_id), x] = (data.loc[('Current Liabilities', stock_id), x] / balance_sheet_table.loc[('Net Assets', stock_id), x]) * 100
        balance_sheet_table.loc[('Net Working Capital %', stock_id), x] = (balance_sheet_table.loc[('Net Working Capital', stock_id), x] / balance_sheet_table.loc[('Net Assets', stock_id), x]) * 100
        balance_sheet_table.loc[('Debt %', stock_id), x] = (balance_sheet_table.loc[('Debt', stock_id), x] / balance_sheet_table.loc[('Net Assets', stock_id), x]) * 100
        balance_sheet_table.loc[('Net Working Capital vs Debt Ratio', stock_id), x] = (balance_sheet_table.loc[('Net Working Capital', stock_id), x] / balance_sheet_table.loc[('Debt', stock_id), x])

balance_sheet_table


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Net Working Capital %', 'Debt %']

ax1.plot(balance_sheet_table.columns, balance_sheet_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(balance_sheet_table.columns, balance_sheet_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')
ax1.plot(balance_sheet_table.columns, balance_sheet_table.loc[metrics[1], 'AAPL'], label=f'AAPL - {metrics[1]}', linestyle='--', marker='x', color='#5DBB63')
ax1.plot(balance_sheet_table.columns, balance_sheet_table.loc[metrics[1], 'NVDA'], label=f'NVDA - {metrics[1]}', linestyle='--', marker='x', color='#F28500')

ax1.set_xlabel('Years')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')


plt.title('Net Working Capital % and Debt %')
plt.grid(True)
plt.show()


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Net Working Capital vs Debt Ratio']

# Plot Return on Net Assets %
ax1.plot(balance_sheet_table.columns, balance_sheet_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(balance_sheet_table.columns, balance_sheet_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.set_ylabel(metrics[0], color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

plt.title('Net Working Capital vs Debt Ratio')
plt.grid(True)
plt.show()


fact = ['Return on Net Assets %', 'Profit Margin %', 'Net Asset Turnover']
multi_index = pd.MultiIndex.from_product([fact, stock_ids], names=['fact', 'stock_id'])
profitability_ratios_table = pd.DataFrame(index=multi_index, columns=years)

for x in years:
    for stock_id in stock_ids:
        profitability_ratios_table.loc[('Return on Net Assets %', stock_id), x] = summary_table.loc[('Return on Net Assets %', stock_id), x]
        profitability_ratios_table.loc[('Profit Margin %', stock_id), x] = (summary_table.loc[('Operating Profit', stock_id), x] / summary_table.loc[('Sales Revenue', stock_id), x]) * 100
        profitability_ratios_table.loc[('Net Asset Turnover', stock_id), x] = summary_table.loc[('Sales Revenue', stock_id), x] / summary_table.loc[('Net Assets', stock_id), x]

profitability_ratios_table


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Return on Net Assets %']

ax1.plot(profitability_ratios_table.columns, profitability_ratios_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(profitability_ratios_table.columns, profitability_ratios_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

plt.title('Return on Net Assets')
plt.grid(True)
plt.show()


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Profit Margin %']

ax1.plot(profitability_ratios_table.columns, profitability_ratios_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(profitability_ratios_table.columns, profitability_ratios_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

plt.title('Profit Margin %')
plt.grid(True)
plt.show()


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Net Asset Turnover']

ax1.plot(profitability_ratios_table.columns, profitability_ratios_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(profitability_ratios_table.columns, profitability_ratios_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

plt.title('Net Asset Turnover')
plt.grid(True)
plt.show()


fact = ['Current ratio', 'Acid test ratio']
multi_index = pd.MultiIndex.from_product([fact, stock_ids], names=['fact', 'stock_id'])
liquidity_ratios_table = pd.DataFrame(index=multi_index, columns=years)

for x in years:
    for stock_id in stock_ids:
        liquidity_ratios_table.loc[('Current ratio', stock_id), x] = data.loc[('Current Assets', stock_id), x] / data.loc[('Current Liabilities', stock_id), x]
        liquidity_ratios_table.loc[('Acid test ratio', stock_id), x] = (data.loc[('Trade Debtors', stock_id), x] + data.loc[('Cash and Cash Equivalents', stock_id), x]) / data.loc[('Current Liabilities', stock_id), x]

liquidity_ratios_table


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Current ratio', 'Acid test ratio']

ax1.plot(liquidity_ratios_table.columns, liquidity_ratios_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(liquidity_ratios_table.columns, liquidity_ratios_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.set_ylabel(metrics[0], color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(liquidity_ratios_table.columns, liquidity_ratios_table.loc[metrics[1], 'AAPL'], label=f'AAPL - {metrics[1]}', linestyle='--', marker='x', color='#5DBB63')
ax2.plot(liquidity_ratios_table.columns, liquidity_ratios_table.loc[metrics[1], 'NVDA'], label=f'NVDA - {metrics[1]}', linestyle='--', marker='x', color='#F28500')

ax2.set_ylabel(metrics[1], color='red')
ax2.tick_params(axis='y', labelcolor='red')
ax2.legend(loc='upper right')

plt.title('Current ratio and Acid test ratio over Years')
plt.grid(True)
plt.show()


fact = ['Operating Cash Flow ratio', 'Cash Flow Margin ratio', 'Cash Flow to Debt ratio']
multi_index = pd.MultiIndex.from_product([fact, stock_ids], names=['fact', 'stock_id'])
cash_flow_ratio_table = pd.DataFrame(index=multi_index, columns=years)

for x in years:
    for stock_id in stock_ids:
        cash_flow_ratio_table.loc[('Operating Cash Flow ratio', stock_id), x] = data.loc[('Net Cash Flow from Operating Activities', stock_id), x] / data.loc[('Current Liabilities', stock_id), x]
        cash_flow_ratio_table.loc[('Cash Flow Margin ratio', stock_id), x] = (data.loc[('Net Cash Flow from Operating Activities', stock_id), x] / summary_table.loc[('Sales Revenue', stock_id), x]) * 100
        cash_flow_ratio_table.loc[('Cash Flow to Debt ratio', stock_id), x] = (data.loc[('Net Cash Flow from Operating Activities', stock_id), x] / (data.loc[('Current Liabilities', stock_id), x] + data.loc[('Non-current Liabilities', stock_id), x])) * 100
    
cash_flow_ratio_table


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Operating Cash Flow ratio']

ax1.plot(cash_flow_ratio_table.columns, cash_flow_ratio_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(cash_flow_ratio_table.columns, cash_flow_ratio_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax1.set_xlabel('Years')
ax1.set_ylabel(metrics[0], color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.legend(loc='upper left')

plt.title('Operating Cash Flow ratio')
plt.grid(True)
plt.show()


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Cash Flow Margin ratio']

ax1.plot(cash_flow_ratio_table.columns, cash_flow_ratio_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(cash_flow_ratio_table.columns, cash_flow_ratio_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax2.set_ylabel(metrics[0], color='red')
ax2.tick_params(axis='y', labelcolor='red')
ax2.legend(loc='upper right')

plt.title('Cash Flow Margin ratio')
plt.grid(True)
plt.show()


fig, ax1 = plt.subplots(figsize=(10, 6))
metrics = ['Cash Flow to Debt ratio']

ax1.plot(cash_flow_ratio_table.columns, cash_flow_ratio_table.loc[metrics[0], 'AAPL'], label=f'AAPL - {metrics[0]}', linestyle='-', marker='o', color='#3CB043')
ax1.plot(cash_flow_ratio_table.columns, cash_flow_ratio_table.loc[metrics[0], 'NVDA'], label=f'NVDA - {metrics[0]}', linestyle='-', marker='o', color='#F04A00')

ax2.set_ylabel(metrics[0], color='red')
ax2.tick_params(axis='y', labelcolor='red')
ax2.legend(loc='upper right')

plt.title('Cash Flow to Debt ratio')
plt.grid(True)
plt.show()


cnx.close()

	date	stock_id	open	close	high	low	volume	industry_id	return	cum_returns
0	2013-11-06	WELL	63.00	63.27	63.560	62.8500	1411071	Utilities	NaN	NaN
1	2013-11-07	WELL	62.71	61.43	62.830	61.3700	1526318	Utilities	-0.029082	0.970918
2	2013-11-08	WELL	61.00	60.41	61.000	59.5000	2373307	Utilities	-0.016604	0.954797
3	2013-11-11	WELL	60.41	60.08	60.840	59.9200	1692912	Utilities	-0.005463	0.949581
4	2013-11-12	WELL	60.00	59.64	60.190	59.3400	1683871	Utilities	-0.007324	0.942627
...	...	...	...	...	...	...	...	...	...	...
138375	2023-10-30	CMCSA	39.96	40.45	40.620	39.9400	18202260	Communication Services	0.020434	1.692115
138376	2023-10-31	CMCSA	40.70	41.29	41.340	40.6688	18860960	Communication Services	0.020766	1.727254
138377	2023-11-01	CMCSA	41.37	41.82	42.135	41.3400	19505410	Communication Services	0.012836	1.749425
138378	2023-11-02	CMCSA	41.86	42.50	42.580	41.8600	16755390	Communication Services	0.016260	1.777871
138379	2023-11-03	CMCSA	42.86	43.18	43.420	42.8201	19005630	Communication Services	0.016000	1.806317

	id	namespace	fact	value	units	start_date	end_date	stock_id	year
19	20	us-gaap	AccountsPayableCurrent	64115000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	0
20	21	us-gaap	AccountsReceivableNetCurrent	28184000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	0
21	22	us-gaap	AssetsCurrent	135405000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	0
22	23	us-gaap	AssetsNoncurrent	217350000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	0
23	24	us-gaap	CashAndCashEquivalentsAtCarryingValue	23646000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	0
...	...	...	...	...	...	...	...	...	...
350	768	us-gaap	CostOfGoodsAndServicesSold	2082030000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	8
351	769	us-gaap	OtherLiabilitiesCurrent	602807000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	8
352	770	us-gaap	OtherAssetsCurrent	70174000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	8
353	771	us-gaap	LongTermDebtCurrent	0.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	8
354	772	us-gaap	LongTermDebtNoncurrent	1384342000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	8

	id	namespace	fact	value	units	start_date	end_date	stock_id	year
19	20	us-gaap	AccountsPayableCurrent	64115000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	2022
20	21	us-gaap	AccountsReceivableNetCurrent	28184000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	2022
21	22	us-gaap	AssetsCurrent	135405000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	2022
22	23	us-gaap	AssetsNoncurrent	217350000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	2022
23	24	us-gaap	CashAndCashEquivalentsAtCarryingValue	23646000000.0000	USD	2022-09-24 00:00:00	2022-09-24 00:00:00	AAPL	2022
...	...	...	...	...	...	...	...	...	...
350	768	us-gaap	CostOfGoodsAndServicesSold	2082030000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	2014
351	769	us-gaap	OtherLiabilitiesCurrent	602807000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	2014
352	770	us-gaap	OtherAssetsCurrent	70174000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	2014
353	771	us-gaap	LongTermDebtCurrent	0.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	2014
354	772	us-gaap	LongTermDebtNoncurrent	1384342000.0000	USD	2015-01-29 00:00:00	2015-01-29 00:00:00	NVDA	2014

	year	2014	2015	2016	2017	2018	2019	2020	2021	2022
fact	stock_id
AccountsPayableCurrent	AAPL	30196000000.0	35490000000.0	37294000000.0	49049000000.0	55888000000.0	46236000000.0	42296000000.0	54763000000.0	64115000000.0
AccountsPayableCurrent	NVDA	293223000.0	296000000.0	485000000.0	596000000.0	511000000.0	687000000.0	1201000000.0	1783000000.0	1193000000.0
AccountsReceivableNetCurrent	AAPL	17460000000.0	16849000000.0	15754000000.0	17874000000.0	23186000000.0	22926000000.0	16120000000.0	26278000000.0	28184000000.0
AccountsReceivableNetCurrent	NVDA	473637000.0	505000000.0	826000000.0	1265000000.0	1424000000.0	1657000000.0	2429000000.0	4650000000.0	3827000000.0
AssetsCurrent	AAPL	68531000000.0	89378000000.0	106869000000.0	128645000000.0	131339000000.0	162819000000.0	143713000000.0	134836000000.0	135405000000.0
AssetsCurrent	NVDA	5713297000.0	6053000000.0	8536000000.0	9255000000.0	10557000000.0	13690000000.0	16055000000.0	28829000000.0	23073000000.0
AssetsNoncurrent	AAPL	163308000000.0	201101000000.0	214817000000.0	246674000000.0	234386000000.0	175697000000.0	180175000000.0	216166000000.0	217350000000.0
AssetsNoncurrent	NVDA	1488000000.0	1317000000.0	1305000000.0	1986000000.0	2735000000.0	3625000000.0	12736000000.0	15358000000.0	18109000000.0
CashAndCashEquivalentsAtCarryingValue	AAPL	13844000000.0	21120000000.0	20484000000.0	20289000000.0	25913000000.0	48844000000.0	38016000000.0	34940000000.0	23646000000.0
CashAndCashEquivalentsAtCarryingValue	NVDA	496654000.0	596000000.0	1766000000.0	4002000000.0	782000000.0	10896000000.0	847000000.0	1990000000.0	3389000000.0
CommonStockDividendsPerShareDeclared	AAPL	1.82	1.98	2.18	2.4	2.72	3.0	0.795	0.85	0.9
CommonStockDividendsPerShareDeclared	NVDA	0.34	0.115	NaN	NaN	NaN	NaN	NaN	NaN	0.16
CostOfGoodsAndServicesSold	AAPL	112258000000.0	140089000000.0	131376000000.0	141048000000.0	163756000000.0	161782000000.0	169559000000.0	212981000000.0	223546000000.0
CostOfGoodsAndServicesSold	NVDA	2082030000.0	2199000000.0	2847000000.0	3892000000.0	4545000000.0	4150000000.0	6279000000.0	9439000000.0	11618000000.0
EarningsPerShareDiluted	AAPL	6.45	9.22	8.31	9.21	11.91	11.89	3.28	5.61	6.11
EarningsPerShareDiluted	NVDA	1.12	1.08	2.57	4.82	6.63	4.52	6.9	3.85	1.74
GrossProfit	AAPL	70537000000.0	93626000000.0	84263000000.0	88186000000.0	101839000000.0	98392000000.0	104956000000.0	152836000000.0	170782000000.0
GrossProfit	NVDA	2599477000.0	2811000000.0	4063000000.0	5822000000.0	7171000000.0	6768000000.0	10396000000.0	17475000000.0	15356000000.0
InventoryNet	AAPL	2111000000.0	2349000000.0	2132000000.0	4855000000.0	3956000000.0	4106000000.0	4061000000.0	6580000000.0	4946000000.0
InventoryNet	NVDA	482893000.0	418000000.0	794000000.0	796000000.0	1575000000.0	979000000.0	1826000000.0	2605000000.0	5159000000.0
LiabilitiesCurrent	AAPL	63448000000.0	80610000000.0	79006000000.0	100814000000.0	116866000000.0	105718000000.0	105392000000.0	125481000000.0	153982000000.0
LiabilitiesCurrent	NVDA	896030000.0	2351000000.0	1788000000.0	1153000000.0	1329000000.0	1784000000.0	3925000000.0	4335000000.0	6563000000.0
LiabilitiesNoncurrent	AAPL	56844000000.0	90514000000.0	114431000000.0	140458000000.0	141712000000.0	142310000000.0	153157000000.0	162431000000.0	148101000000.0
LiabilitiesNoncurrent	NVDA	1887000000.0	463000000.0	2260000000.0	2617000000.0	2621000000.0	3327000000.0	7973000000.0	13240000000.0	12518000000.0
LongTermDebtCurrent	AAPL	0.0	2500000000.0	3500000000.0	6496000000.0	8784000000.0	10260000000.0	8773000000.0	9613000000.0	11128000000.0
LongTermDebtCurrent	NVDA	0.0	1413000000.0	796000000.0	15000000.0	0.0	0.0	999000000.0	0.0	1250000000.0
LongTermDebtNoncurrent	AAPL	28987000000.0	53463000000.0	75427000000.0	97207000000.0	93735000000.0	91807000000.0	98667000000.0	109106000000.0	98959000000.0
LongTermDebtNoncurrent	NVDA	1384342000.0	0.0	1983000000.0	1985000000.0	1988000000.0	5964000000.0	5964000000.0	10946000000.0	9703000000.0
NetCashProvidedByUsedInOperatingActivities	AAPL	59713000000.0	81266000000.0	65824000000.0	63598000000.0	77434000000.0	69391000000.0	80674000000.0	104038000000.0	122151000000.0
NetCashProvidedByUsedInOperatingActivities	NVDA	905656000.0	1175000000.0	1672000000.0	3502000000.0	3743000000.0	4761000000.0	5822000000.0	9108000000.0	5641000000.0
OperatingIncomeLoss	AAPL	52503000000.0	71230000000.0	60024000000.0	61344000000.0	70898000000.0	63930000000.0	66288000000.0	108949000000.0	119437000000.0
OperatingIncomeLoss	NVDA	758989000.0	747000000.0	1934000000.0	3210000000.0	3804000000.0	2846000000.0	4532000000.0	10041000000.0	4224000000.0
OtherAssetsCurrent	AAPL	9806000000.0	9539000000.0	8283000000.0	13936000000.0	12087000000.0	12352000000.0	11264000000.0	14111000000.0	21223000000.0
OtherAssetsCurrent	NVDA	70174000.0	93000000.0	118000000.0	86000000.0	136000000.0	157000000.0	239000000.0	366000000.0	791000000.0
OtherLiabilitiesCurrent	AAPL	0.0	0.0	0.0	0.0	32687000000.0	37720000000.0	42684000000.0	47493000000.0	60845000000.0
OtherLiabilitiesCurrent	NVDA	602807000.0	642000000.0	507000000.0	542000000.0	818000000.0	1097000000.0	1725000000.0	2552000000.0	4120000000.0
RevenueFromContractWithCustomerExcludingAssessedTax	AAPL	182795000000.0	233715000000.0	215639000000.0	229234000000.0	265595000000.0	260174000000.0	274515000000.0	365817000000.0	394328000000.0
RevenueFromContractWithCustomerExcludingAssessedTax	NVDA	4682000000.0	5010000000.0	6910000000.0	9714000000.0	11716000000.0	10918000000.0	16675000000.0	26914000000.0	26974000000.0

	year	2014	2015	2016	2017	2018	2019	2020	2021	2022
fact	stock_id
Trade Creditors	AAPL	30196.0	35490.0	37294.0	49049.0	55888.0	46236.0	42296.0	54763.0	64115.0
Trade Creditors	NVDA	293.223	296.0	485.0	596.0	511.0	687.0	1201.0	1783.0	1193.0
Trade Debtors	AAPL	17460.0	16849.0	15754.0	17874.0	23186.0	22926.0	16120.0	26278.0	28184.0
Trade Debtors	NVDA	473.637	505.0	826.0	1265.0	1424.0	1657.0	2429.0	4650.0	3827.0
Current Assets	AAPL	68531.0	89378.0	106869.0	128645.0	131339.0	162819.0	143713.0	134836.0	135405.0
Current Assets	NVDA	5713.297	6053.0	8536.0	9255.0	10557.0	13690.0	16055.0	28829.0	23073.0
Non-current Assets	AAPL	163308.0	201101.0	214817.0	246674.0	234386.0	175697.0	180175.0	216166.0	217350.0
Non-current Assets	NVDA	1488.0	1317.0	1305.0	1986.0	2735.0	3625.0	12736.0	15358.0	18109.0
Cash and Cash Equivalents	AAPL	13844.0	21120.0	20484.0	20289.0	25913.0	48844.0	38016.0	34940.0	23646.0
Cash and Cash Equivalents	NVDA	496.654	596.0	1766.0	4002.0	782.0	10896.0	847.0	1990.0	3389.0
Dividends	AAPL	1.82	1.98	2.18	2.4	2.72	3.0	0.795	0.85	0.9
Dividends	NVDA	0.34	0.115	NaN	NaN	NaN	NaN	NaN	NaN	0.16
Cost of Sales	AAPL	112258.0	140089.0	131376.0	141048.0	163756.0	161782.0	169559.0	212981.0	223546.0
Cost of Sales	NVDA	2082.03	2199.0	2847.0	3892.0	4545.0	4150.0	6279.0	9439.0	11618.0
Earnings Per Share	AAPL	6.45	9.22	8.31	9.21	11.91	11.89	3.28	5.61	6.11
Earnings Per Share	NVDA	1.12	1.08	2.57	4.82	6.63	4.52	6.9	3.85	1.74
Gross Profit	AAPL	70537.0	93626.0	84263.0	88186.0	101839.0	98392.0	104956.0	152836.0	170782.0
Gross Profit	NVDA	2599.477	2811.0	4063.0	5822.0	7171.0	6768.0	10396.0	17475.0	15356.0
Inventories	AAPL	2111.0	2349.0	2132.0	4855.0	3956.0	4106.0	4061.0	6580.0	4946.0
Inventories	NVDA	482.893	418.0	794.0	796.0	1575.0	979.0	1826.0	2605.0	5159.0
Current Liabilities	AAPL	63448.0	80610.0	79006.0	100814.0	116866.0	105718.0	105392.0	125481.0	153982.0
Current Liabilities	NVDA	896.03	2351.0	1788.0	1153.0	1329.0	1784.0	3925.0	4335.0	6563.0
Non-current Liabilities	AAPL	56844.0	90514.0	114431.0	140458.0	141712.0	142310.0	153157.0	162431.0	148101.0
Non-current Liabilities	NVDA	1887.0	463.0	2260.0	2617.0	2621.0	3327.0	7973.0	13240.0	12518.0
Current Long Term Debt	AAPL	0.0	2500.0	3500.0	6496.0	8784.0	10260.0	8773.0	9613.0	11128.0
Current Long Term Debt	NVDA	0.0	1413.0	796.0	15.0	0.0	0.0	999.0	0.0	1250.0
Non-current Long Term Debt	AAPL	28987.0	53463.0	75427.0	97207.0	93735.0	91807.0	98667.0	109106.0	98959.0
Non-current Long Term Debt	NVDA	1384.342	0.0	1983.0	1985.0	1988.0	5964.0	5964.0	10946.0	9703.0
Net Cash Flow from Operating Activities	AAPL	59713.0	81266.0	65824.0	63598.0	77434.0	69391.0	80674.0	104038.0	122151.0
Net Cash Flow from Operating Activities	NVDA	905.656	1175.0	1672.0	3502.0	3743.0	4761.0	5822.0	9108.0	5641.0
Operating Profit	AAPL	52503.0	71230.0	60024.0	61344.0	70898.0	63930.0	66288.0	108949.0	119437.0
Operating Profit	NVDA	758.989	747.0	1934.0	3210.0	3804.0	2846.0	4532.0	10041.0	4224.0
Other Current Assets	AAPL	9806.0	9539.0	8283.0	13936.0	12087.0	12352.0	11264.0	14111.0	21223.0
Other Current Assets	NVDA	70.174	93.0	118.0	86.0	136.0	157.0	239.0	366.0	791.0
Other Current Liabilities	AAPL	0.0	0.0	0.0	0.0	32687.0	37720.0	42684.0	47493.0	60845.0
Other Current Liabilities	NVDA	602.807	642.0	507.0	542.0	818.0	1097.0	1725.0	2552.0	4120.0
Total Net Sales	AAPL	182795.0	233715.0	215639.0	229234.0	265595.0	260174.0	274515.0	365817.0	394328.0
Total Net Sales	NVDA	4682.0	5010.0	6910.0	9714.0	11716.0	10918.0	16675.0	26914.0	26974.0

Exploratory Data Analysis for Maximizing Stock Market Returns and Managing Risk¶

1 Project Overview¶

1.1 Objective¶

1.2 Methodology¶

2 Data Analysis of Stock Price¶

2.1 Retrieve Stock Prices and Industry¶

2.1.1 Aggregate the Returns and Cumulative Return Values for each stock¶

2.2 Plot Close and Cumulative Returns¶

2.2.1 Close Price¶

2.2.2 Cumulative Returns¶

2.3 Analysis on Historical Price Data¶

3 Data Analysis of Financial Metrics¶

3.1 Retrieve Financial Metrics for NVDA and AAPL¶

3.2 Calculation of Metrics¶

3.2.1 10-year Summary Overview¶

3.2.2 Income Statement Analysis¶

3.2.3 Balance Sheet Analysis¶

3.2.4 Profitability Ratios¶

3.2.5 Liquidity Ratios¶

3.2.6 Cash Flow Ratios¶

4 Conclusion¶

4.1 Analysis on Financial Metrics¶

4.2 Potential Improvements¶

	year	2014	2015	2016	2017	2018	2019	2020	2021	2022
fact	stock_id
Sales Revenue	AAPL	182795.0	233715.0	215639.0	229234.0	265595.0	260174.0	274515.0	365817.0	394328.0
Sales Revenue	NVDA	4682.0	5010.0	6910.0	9714.0	11716.0	10918.0	16675.0	26914.0	26974.0
Operating Profit	AAPL	52503.0	71230.0	60024.0	61344.0	70898.0	63930.0	66288.0	108949.0	119437.0
Operating Profit	NVDA	758.989	747.0	1934.0	3210.0	3804.0	2846.0	4532.0	10041.0	4224.0
Net Assets	AAPL	168391.0	209869.0	242680.0	274505.0	248859.0	232798.0	218496.0	225521.0	198773.0
Net Assets	NVDA	6305.267	5019.0	8053.0	10088.0	11963.0	15531.0	24866.0	39852.0	34619.0
Return on Net Assets %	AAPL	31.17922	33.94022	24.733806	22.347134	28.489225	27.461576	30.338313	48.309913	60.087135
Return on Net Assets %	NVDA	12.037381	14.883443	24.015895	31.819984	31.798044	18.324641	18.22569	25.195724	12.201392
Current Liabilities Ratio %	AAPL	37.678973	38.409675	32.555629	36.725743	46.960729	45.411902	48.235208	55.640495	77.466255
Current Liabilities Ratio %	NVDA	14.210818	46.842	22.202906	11.429421	11.109254	11.486704	15.784605	10.877748	18.957798

	year	2014	2015	2016	2017	2018	2019	2020	2021	2022
fact	stock_id
Current ratio	AAPL	1.080113	1.108771	1.352669	1.276063	1.123843	1.540126	1.363604	1.074553	0.879356
Current ratio	NVDA	6.376234	2.574649	4.774049	8.026886	7.943567	7.673767	4.090446	6.650288	3.515618
Acid test ratio	AAPL	0.49338	0.471021	0.458674	0.378549	0.420131	0.678882	0.513663	0.487867	0.336598
Acid test ratio	NVDA	1.082878	0.468311	1.449664	4.568083	1.659895	7.036435	0.83465	1.531719	1.099497

	year	2014	2015	2016	2017	2018	2019	2020	2021	2022
fact	stock_id
Operating Cash Flow ratio	AAPL	0.941133	1.008138	0.833152	0.630845	0.662588	0.656378	0.765466	0.829114	0.793281
Operating Cash Flow ratio	NVDA	1.010743	0.499787	0.935123	3.037294	2.816403	2.668722	1.483312	2.101038	0.859515
Cash Flow Margin ratio	AAPL	32.666648	34.77141	30.525091	27.743703	29.154916	26.670997	29.387829	28.439903	30.977004
Cash Flow Margin ratio	NVDA	19.343358	23.453094	24.196816	36.05106	31.947764	43.606888	34.914543	33.841124	20.912731
Cash Flow to Debt ratio	AAPL	49.640043	47.48954	34.02865	26.359462	29.94609	27.977083	31.202596	36.135347	40.436238
Cash Flow to Debt ratio	NVDA	32.542085	41.755508	41.304348	92.891247	94.759494	93.152025	48.932594	51.823613	29.56344